Analyzing crash data effectively

November 30, 2017

A favorite trick people love to use when supporting their narrative is to manipulate or misrepresent statistics. Sometimes this is a result of ignorance and not knowing how to read data. Other times, it’s malicious. I’m trying to figure which category both the Ohio Department of Public Safety and the Ohio Department of Transportation fall under when they made recommendations for 70 mph roadways back in October.

Background

In 2013, Ohio increased speed limits on rural interstate routes and freeways from 65 mph to 70 mph. After analyzing crash data two years prior to the change and two years after, the two departments concluded that traffic crashes increased after implementing the higher speed limits.

As a result of their findings, ODOT and ODPS recommended a “targeted enforcement and media campaign in three high-crash corridors for six months in an effort to reduce the number of speeding motorists and traffic crashes.”

Furthermore, ODOT and ODPS recommended a speed limit reduction back to 65 mph on one other corridor for six months. The idea here is to determine whether or not reducing the speed limit will have a reverse effect, i.e. fewer crashes.

The crash data

The claims of increased crashes come from a speed limit analysis that was published in June. ODPS found the following within the study:

24 percent increase in crashes on 70 mph roads from before (7,884) to after (9,812) the speed limit change. This includes 22 percent more fatal and injury crashes (1,915 to 2,341).

At face value, those numbers raise a valid argument in favor of rethinking or reevaluating the speed limit change. Crashes went up significantly after speed limits went up. Pretty simple.

Except, when it comes to crash data, it’s really more complex than that.

Analyzing crash data

The most common mistake armchair researchers/scientists make when extrapolating information from data is confusing causation with correlation. This seems to be extremely common in Washington D.C., as well as in local and state governments and agencies.

For example: Let’s say I have not gotten sick since I started taking a daily multivitamin. Is there a positive correlation with my health and vitamins? Absolutely. Does that mean multivitamins prevent you from getting sick? Maybe or maybe not.

You can’t definitively determine one way or the other with that information and without exploring all other potential factors. It’s entirely possible that my improved health has to do with a diet change at the same time or perhaps the time frame of my data points occur in the spring and summer, rather than the winter, when I’m more susceptible to illness. Those possibilities are not reflected in the raw data.

Only after doing research with the scientific method, such as a controlled double blind study with a placebo, would I know whether or not the multivitamin caused the improvement in my health.

Take that logic and apply to the ODPS’ raw crash data: Crashes increased after speed limit increase compared to before speed limit increase.

Is there a positive correlation with the speed limit and number of crashes? Yes. Does this mean raising the speed limit caused the increase in crashes? You cannot determine that with the limited data available.

Nationwide, vehicle-miles-traveled began to dramatically increase beginning around 2015. This was the result of a rebounding economy and ultra-low fuel prices. When people are making more money and gas is cheap, more people drive more miles. An increase in number of vehicles on the road combined with more frequency and distance will likely have the unintended consequence of more crashes.

So what did ODPS’ speed limit analysis say about crashes adjusted to VMT?

Analysis of Ohio’s crash data

Not much.

In its 26-page speed limit analysis, VMT was mentioned in only one paragraph. Here’s what was said:

During the last 23 years, vehicle miles traveled in Ohio (measured in millions; MVMT) increased 21 percent (from 97,522 MVMT in 1993 to 117,829 MVMT in 2015; see Figure 1). Conversely, traffic fatalities decreased 25 percent during those same years (from 1,479 to 1,110). Although Ohio has experienced long-term trends of decreased fatalities despite increased exposure (i.e., MVMT), it is important to examine the effects of the recent speed limit increase on Ohio’s rural freeways and interstates in 2013.

That’s a pretty compelling argument. But there’s one problem. That data is based on a 23-year time frame. The data in the speed limit analysis is based on two two-year times frames, one before (2011-12) and one after (2014-15) the speed limit change. A 23-year pattern may not be reflected in a two-year database.

Locations of the three corridors recommended for targeted enforcement are in Licking, Ashland and Union Counties. Here’s the total vehicle miles traveled data for those counties in the years studied (by thousands of daily VMT):

County

2011

2012

2014

2015

Ashland

1,777.76

1,808.03

1,858.97

2,060.21

Licking

5,077.82

5,070.39

5,127.92

5,298.23

Union

1,780.20

1,772.20

1,860.17

2,174.44

In two of those counties, VMT decreased from 2011 to 2012, a deviation from a 23-year trend.

However, from 2012 to 2015, vehicle miles traveled increased by 1.6 percent in Ashland County, 2.2 percent in Union County and 4.3 percent in Licking County.

Perhaps this was a major contributor to the increase in crashes.

Also, check out this graph included in the study:

This is the graph that accompanies the above quote “Although Ohio has experienced long-term trends of decreased fatalities despite increased exposure, it is important to examine the effects of the recent speed limit increase on Ohio’s rural freeways and interstates in 2013.”

But check out what happens right before the two-year period before the speed limit change. It skyrockets upwards then plummets downwards during the study period. Then in 2014 (when the “after” period begins), fatalities trend back upwards, essentially normalizing the levels right before 2011. Also notice how the increase of traffic fatalities after 2013 follows the same linear path of MVMT.

This analysis is not found in the study.

Recommendations

To start, it is entirely possible that increasing the speed limit to 70 mph from 65 mph was a major contributor to the increase in crashes within the studied time frame. I’m just not comfortable reaching that conclusion based on the limited, raw data.

With that said, I recommend that ODPS and ODOT reevaluate the numbers by adjusting traffic crashes to vehicle miles traveled. It may also help to break down the crashes according to cause of crash, e.g. distracted driving, speeding, weather, etc.

My one other recommendation is already being considered by ODPS and ODOT, which is to revert back to 65 mph and see where that data takes them. Have two-year data on before 70 mph change, after 70 mph change, after going back to 65 mph and then increasing to 70 mph again and evaluating that data. Again, adjusted to vehicle miles traveled.

Reach a conclusion with those numbers, and I will be more comfortable with the results.

Tyson Fisher, staff writer and research associate, joined Land Line Magazine in March 2014. An award-winning journalist and tireless researcher, his news reports, features and blogs bring depth to our editorial content, backed with solid detail. Tyson received his journalism degree from the University of Missouri-Kansas City.

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